Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction

Wayne Joubert; Deborah Weighill; David Kainer; Sharlee Climer; Amy Justice; Kjiersten Fagnan; Daniel Jacobson

doi:10.1109/SC.2018.00060

Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction

Wayne Joubert, Deborah Weighill, David Kainer, Sharlee Climer, Amy Justice, Kjiersten Fagnan, Daniel Jacobson

Computational and Predictive Biology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

43 Scopus citations

Abstract

We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, transfers and computations, high efficiency with respect to single GPU kernel performance is maintained up to the full Titan and Summit systems. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.

Original language	English
Title of host publication	Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	717-730
Number of pages	14
ISBN (Electronic)	9781538683842
DOIs	https://doi.org/10.1109/SC.2018.00060
State	Published - Jul 2 2018
Event	2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 - Dallas, United States Duration: Nov 11 2018 → Nov 16 2018

Publication series

Name	Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018

Conference

Conference	2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018
Country/Territory	United States
City	Dallas
Period	11/11/18 → 11/16/18

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The United States Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, world-wide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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10.1109/SC.2018.00060

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Joubert, W., Weighill, D., Kainer, D., Climer, S., Justice, A., Fagnan, K., & Jacobson, D. (2018). Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. In Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018 (pp. 717-730). Article 8665788 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SC.2018.00060

Joubert, Wayne ; Weighill, Deborah ; Kainer, David et al. / Attacking the opioid epidemic : Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 717-730 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018).

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abstract = "We describe the CoMet application for large-scale epistatic Genome-Wide Association Studies (eGWAS) and pleiotropy studies. High performance is attained by transforming the underlying vector comparison methods into highly performant generalized distributed dense linear algebra operations. The 2-way and 3-way Proportional Similarity metric and Custom Correlation Coefficient are implemented using native or adapted GEMM kernels optimized for GPU architectures. By aggressive overlapping of communications, transfers and computations, high efficiency with respect to single GPU kernel performance is maintained up to the full Titan and Summit systems. Nearly 300 quadrillion element comparisons per second and over 2.3 mixed precision ExaOps are reached on Summit by use of Tensor Core hardware on the Nvidia Volta GPUs. Performance is four to five orders of magnitude beyond comparable state of the art. CoMet is currently being used in projects ranging from bioenergy to clinical genomics, including for the genetics of chronic pain and opioid addiction.",

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Joubert, W, Weighill, D, Kainer, D, Climer, S, Justice, A, Fagnan, K & Jacobson, D 2018, Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. in Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018., 8665788, Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, Institute of Electrical and Electronics Engineers Inc., pp. 717-730, 2018 International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018, Dallas, United States, 11/11/18. https://doi.org/10.1109/SC.2018.00060

Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. / Joubert, Wayne; Weighill, Deborah; Kainer, David et al.
Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 717-730 8665788 (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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Joubert W, Weighill D, Kainer D, Climer S, Justice A, Fagnan K et al. Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction. In Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 717-730. 8665788. (Proceedings - International Conference for High Performance Computing, Networking, Storage, and Analysis, SC 2018). doi: 10.1109/SC.2018.00060

Attacking the opioid epidemic: Determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction

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